To advance the state of the art of engineering design, we introduce a new concept on the "robustness" of a structure by measuring its ability to sustain a sudden loss of a part without causing an immediate collapse. The concept is based on the premise that most structures have built-in redundancy such that when the loss of a single part leads to a load redistribution, the "crippled" structure tends to seek a new stability configuration without immediate collapse. This nice property of a "robust" structure, when coupled with a continuous or periodic inspection program using nondestructive evaluation (NDE) techniques, is useful in failure prevention, because such structure is expected to display "measurable" signs of "weakening" long before the onset of catastrophic failure. To quantify this "robustness" concept, we use a large number of simulations to develop a metric to be named the "Robustness Index (RBI)." To illustrate its application, we present two examples: (1) the design of a simple square grillage in support of a water tank, and (2) a 1/12-scale model of the recently collapsed Minnesota bridge. The first example is a "toy" problem, which turned out to be a good vehicle to test the feasibility of the RBI concept. The second example is based on news reports and a 1964 construction drawing of the collapsed bridge available to the public through the internet. It is not a case study for failure analysis, but a useful classroom exercise in an engineering design course. Significance and limitations of this new approach to catastrophic failure avoidance through "robust" design, are discussed.
Proceedings Title: Proceedings of ASME Pressure Vessels and Piping Division Conference
Conference Dates: July 27-31, 2008
Conference Location: Chicago, IL
Pub Type: Conferences
aging structures, analysis of variance, applied mechanics, bridge design, design of experiments, engineering safety, error propagation, finite element method, NDE monitoring, RBI, robust design, robustness index, robustness metric, sensitivity analysis, statistical data analysis, structural robustness analysis, uncertainty analysis